The co-heating test as a means to evaluate the efficiency of thermal retrofit measures applied on residential buildings

In order to reduce the energy use of residential buildings, regional governments in Belgium established, amongst others, mandatory criteria for the energy performance to be achieved after retrofitting. However, due to construction deficiencies, deviating boundary conditions, and nonmodeled physical phenomena and interactions, the actual energy performance may differ significantly from theoretical design value. Several studies indicate this as the performance gap. This paper focuses on analyzing the actual impact of the refurbishment measures applied to a single-family home in Belgium. Hereto, in-situ measurements assessing the building envelope’s thermal performance, described by the overall heat loss coefficient HLC [W/K], are performed both before and after the retrofit. To analyze this HLC, a quasi-steady state test, the so-called co-heating test, has been performed before and after renovation of a single-family home in Belgium, renovated to the nearly Zero Energy Building (nZEB) level. As a result, the HLC determined with linear regression and an Auto-Regressive model with eXogenous inputs (ARX) show similar estimates, except for a smaller confidence interval for the ARX. Furthermore, it is shown that data set lengths shorter than 10 days are quite sensitive to sample times. For our case study, the gap between the theoretical and measured HLC enlarges after retrofit. Finally, the influence of a unheated neighboring zone on the HLC is assessed

Mapping the pitfalls in the characterisation of the heat loss coefficient from on-board monitoring data using ARX models

Several studies have demonstrated the capability of data-driven modelling based on on-site measurements to characterise the thermal performance of building envelopes. Currently, such methods include steady-state and dynamic heating experiments and have mainly been applied to scale models and unoccupied test buildings. Nonetheless, it is proposed to upscale these concepts to characterise the thermal performance of in-use buildings based on on-board monitoring (OBM) devices which gather long-term operational data (e.g., room temperatures, gas and electricity consumption...). It remains, however, to be proven whether in-use data could be a cost-effective, practical and reliable alternative for the dedicated tests whose more intrusive measurements require on-site inspections. Furthermore, it is presently unclear what the optimal experimental design of the OBM would be and which data analysis methods would be adequate. This paper presents a first step in bridging this knowledge gap, by using on-board monitoring data to characterise the overall heat loss coefficient (HLC) [W/K] of an occupied, well-insulated single-family house in the UK. With the aid of a detailed building physical framework and specifically selected data subsets a sensitivity analysis is carried out to analyse the impact of the measurement set-up, the duration of the measurement campaign and the applied data analysis method. Although the exact HLC of the building is unknown and no absolute errors could hence be calculated, this paper provides a new understanding of the decisions that have to be made during the process from design of experiment to data analysis. It is demonstrated that such judgements can lead to differences in the mean HLC estimate of up to 89.5%

Characterization of the Heat Loss Coefficient of Residential Buildings Based on In-Use Monitoring Data

In support of the energy transition in the built environment, knowledge on performance characteristics of the building fabric and technical systems is essential for various applications, such as quality assurance, the development of renovation strategies, or the appraisal of the potential for demand side flexibility. In practice, the available information on the existing building stock is often limited and of poor quality. Furthermore, research shows that results of calculations and simulations made during the design phase significantly differ from the actual as-built performance. Characterization techniques based on on-site measurements, provide valuable alternatives for the assessment of performance indicators, but some of the experimental setups can be perceived as intrusive and costly. Novel techniques, such as 3D Lidar geometry scanning, aerial thermography, smart meters, and IoT sensors provide new opportunities to develop less intrusive and faster identification methods. The first aim of this work is to elucidate the interplay of four aspects involved in building energy performance characterization, namely (1) the applications of characterization, (2) the performance indicators that need to be determined, (3) the characterization methods that can be used and (4) the demands of stakeholders involved. Hereto, an assessment framework is developed which links all these aspects and presents them in the form of a three-dimensional matrix. A potential application of this matrix is for example a tool that guides stakeholders in selecting a suitable characterization method to determine a performance indicator within a specific range of accuracy. Subsequently, the research focuses on one particular performance indicator included in the matrix: the Heat Loss Coefficient or 'HLC'. The HLC describes the insulation quality and airtightness of a building envelope in a single factor. This work explores whether this performance indicator can be assessed for single-family dwellings based on a combination of 'in-use monitoring' and data-driven modeling using steady-state or dynamic analysis methods. In-use monitoring is hereby defined as the monitoring of the energy consumption and interior climate of occupied buildings via non-intrusive sensors. It is investigated how the results of the characterization can be linked to the physical reality. Since several ranges of input data are possible, going from solely smart meter data to a combination of sensor data, data from Building Information Models, surveys and default values, a sensitivity analysis is conducted of the HLC estimate to the amount and accuracy of the input data. Furthermore, the influence of the building considered (e.g. type, insulation quality, heating profile) and data analysis method used on the obtained HLC estimate is analyzed. This dissertation includes case study analyses on both actual measured data and synthetic data derived from energy simulations. This combination allows to examine various scenarios regarding the building type and interior climate, without losing sight of the particularities of on-site collected data. The research confirms the intrinsic capability of HLC characterization based on in-use monitoring data. It is demonstrated that an accuracy of up to 2.5% can be achieved. The characterization accuracy is however strongly dependent on the investigated building and the methodological choices made during the collectionand analysis of the monitoring data, such as the number and position of the temperature sensors installed, the measurement duration, selected data analysis method, or the interpretation of the identified model coefficients. The work concludes by exemplifying how by sensibly selecting the input data and analysis methods, a wide range of in-use characterization methods can be developed, suited for different applications, budgets and timescales.status: publishe

Link between BIM and Energy Simulation

The emerging policies on building energy performance, developed by governments to face growing environmental concerns, stress the need for means to predict the future performance of a scheme in order to ensure that the as-built project will meet regulations. Several methods may enable us to expeditiously make the link between a Building Information Model (BIM) and energy simulation tools during the design process. We can differentiate between energy evaluations accomplished within the BIM software and those requiring data transfer from the model to specific analysis tools. Likewise, we can make a distinction between evaluations applicable during the concept and the design phase. The intention of the research described in this paper was to gain insight into the technical abilities and to point out issues concerning the relation between BIM and energy simulation in the different stages of design, with an emphasis on the conceptual stage. Hence a concise overview of the appropriate literature has been made, followed by five case studies. We get acquainted with an arsenal of tools, some of which still in a scientific stage, and see all kinds of promising developments on the market, both from BIM applications and from energy simulation tools. Software used includes ArchiCAD, Revit, Sefaira, the Space Boundary Tool, EnergyPlus, SketchUp, Open Studio and the EPB software imposed by the Belgian government.status: publishe

Link between BIM and Energy Simulation

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Link between BIM and Energy Simulation

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Experimental analysis of indoor temperature of residential buildings as an input for building simulation tools

The Energy Performance of Buildings Directive (EPBD) requires Member States to assess the energy use of buildings. However, the results heavily depend on the correct implementation of building properties and boundary conditions. This paper challenges the average indoor temperature assumed in Quasi Steady State methods and proposes a temperature profile to improve the accuracy of the simulation. This temperature profile is derived from in-situ measurements of the indoor temperature in nine terraced houses and is characterised by the energy performance of the building, the outdoor climate and the user behaviour. Applying this new temperature profile in the energy simulation model decreases the energy performance gap between simulated and actual energy use.status: publishe

A simulation exercise to improve building energy performance characterization via on-board monitoring

Both a well-designed on-board monitoring campaign and an adequate data-driven statistical modeling method are required to accurately characterize the building’s overall heat transfer coefficient (HTC). In this paper, we reflect on the latter by means of a theoretical deduction of the heat balance equation and case studies on simulation data. We demonstrate the impact of using air temperatures as a proxy for equivalent temperatures and neglecting the intercept when characterizing the HTC using a linear regression method on measurement data.status: publishe